Project E: Data Driven Systems Genetics Workflow for New Experimental Platforms; Elissa J. Chesler, Matthew A. Hibbs (Jackson) Systems genetics experiments typically involve separate acquisition of genotype, gene expression and phenotypic data in a genetically diverse population (Fig. 7 left). Conventional QTL and co-expression methods are applied to these data to construct genotype and phenotype networks. Application of this approach is reliant on existing resources, including a well-established reference genome, a dense genetic marker map often derived relative to the reference genome, and microarrays that are biased toward specific transcript structures and alleles. We will develop an approach that avoids these intrinsic biases through the development and application of high throughput RNA sequencing technology (HTPS) as the sole source of transcription and polymorphism data for an expression QTL experiment (Fig. 7 right). These new methods will minimize initial knowledge requirements. We will create software for our data-driven systems genetics approach, called SEQQTL, for use with highly diverse mouse populations, newly sequenced organisms, or in populations without an established genetic map. We will develop and validate these techniques in the DO mouse population.